Search (89 results, page 1 of 5)

  • × theme_ss:"Informetrie"
  • × year_i:[2000 TO 2010}
  1. Ahlgren, P.; Jarneving, B.; Rousseau, R.: Requirements for a cocitation similarity measure, with special reference to Pearson's correlation coefficient (2003) 0.03
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    Abstract
    Ahlgren, Jarneving, and. Rousseau review accepted procedures for author co-citation analysis first pointing out that since in the raw data matrix the row and column values are identical i,e, the co-citation count of two authors, there is no clear choice for diagonal values. They suggest the number of times an author has been co-cited with himself excluding self citation rather than the common treatment as zeros or as missing values. When the matrix is converted to a similarity matrix the normal procedure is to create a matrix of Pearson's r coefficients between data vectors. Ranking by r and by co-citation frequency and by intuition can easily yield three different orders. It would seem necessary that the adding of zeros to the matrix will not affect the value or the relative order of similarity measures but it is shown that this is not the case with Pearson's r. Using 913 bibliographic descriptions form the Web of Science of articles form JASIS and Scientometrics, authors names were extracted, edited and 12 information retrieval authors and 12 bibliometric authors each from the top 100 most cited were selected. Co-citation and r value (diagonal elements treated as missing) matrices were constructed, and then reconstructed in expanded form. Adding zeros can both change the r value and the ordering of the authors based upon that value. A chi-squared distance measure would not violate these requirements, nor would the cosine coefficient. It is also argued that co-citation data is ordinal data since there is no assurance of an absolute zero number of co-citations, and thus Pearson is not appropriate. The number of ties in co-citation data make the use of the Spearman rank order coefficient problematic.
    Date
    9. 7.2006 10:22:35
  2. Asonuma, A.; Fang, Y.; Rousseau, R.: Reflections on the age distribution of Japanese scientists (2006) 0.03
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    Date
    22. 7.2006 15:26:24
  3. Raan, A.F.J. van: Scaling rules in the science system : influence of field-specific citation characteristics on the impact of research groups (2008) 0.03
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    Date
    22. 3.2009 19:03:12
    Footnote
    Vgl. auch: Costas, R., M. Bordons u. T.N. van Leeuwen u.a.: Scaling rules in the science system: Influence of field-specific citation characteristics on the impact of individual researchers. In: Journal of the American Society for Information Science and Technology. 60(2009) no.4, S.740-753.
  4. Ball, R.: Wissenschaftsindikatoren im Zeitalter digitaler Wissenschaft (2007) 0.02
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    Date
    23.12.2007 19:22:21
  5. Costas, R.; Bordons, M.; Leeuwen, T.N. van; Raan, A.F.J. van: Scaling rules in the science system : Influence of field-specific citation characteristics on the impact of individual researchers (2009) 0.02
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    Date
    22. 3.2009 19:02:48
  6. Nicolaisen, J.: Citation analysis (2007) 0.02
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    Date
    13. 7.2008 19:53:22
  7. Van der Veer Martens, B.: Do citation systems represent theories of truth? (2001) 0.02
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    Date
    22. 7.2006 15:22:28
  8. White, H.D.: Author cocitation analysis and pearson's r (2003) 0.02
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    Abstract
    In their article "Requirements for a cocitation similarity measure, with special reference to Pearson's correlation coefficient," Ahlgren, Jarneving, and Rousseau fault traditional author cocitation analysis (ACA) for using Pearson's r as a measure of similarity between authors because it fails two tests of stability of measurement. The instabilities arise when rs are recalculated after a first coherent group of authors has been augmented by a second coherent group with whom the first has little or no cocitation. However, AJ&R neither cluster nor map their data to demonstrate how fluctuations in rs will mislead the analyst, and the problem they pose is remote from both theory and practice in traditional ACA. By entering their own rs into multidimensional scaling and clustering routines, I show that, despite r's fluctuations, clusters based an it are much the same for the combined groups as for the separate groups. The combined groups when mapped appear as polarized clumps of points in two-dimensional space, confirming that differences between the groups have become much more important than differences within the groups-an accurate portrayal of what has happened to the data. Moreover, r produces clusters and maps very like those based an other coefficients that AJ&R mention as possible replacements, such as a cosine similarity measure or a chi square dissimilarity measure. Thus, r performs well enough for the purposes of ACA. Accordingly, I argue that qualitative information revealing why authors are cocited is more important than the cautions proposed in the AJ&R critique. I include notes an topics such as handling the diagonal in author cocitation matrices, lognormalizing data, and testing r for significance.
  9. Liang, L.: R-Sequences : relative indicators for the rhythm of science (2005) 0.02
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    Abstract
    Like most activities in the world, scientific evolution has its own rhythm. How can this evolutionary rhythm be described and made visible? Do different fields have different rhythms, and how can they be measured? In order to answer these questions a relative indicator, called R-sequence, was designed. This indicator is time dependent, derived from publication and citation data, but independent of the absolute number of publications, as weIl as the absolute number of citations, and can therefore be used in a comparison of different scientific fields, nations, Institutes, or journals. Two caiculation methods of the R-sequence-the triangle method and the parallelogram method-are introduced. As a case study JASIS(T)'s R-sequence has been obtained.
  10. Egghe, L.; Liang, L.; Rousseau, R.: Fundamental properties of rhythm sequences (2008) 0.02
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    Abstract
    Fundamental mathematical properties of rhythm sequences are studied. In particular, a set of three axioms for valid rhythm indicators is proposed, and it is shown that the R-indicator satisfies only two out of three but that the R-indicator satisfies all three. This fills a critical, logical gap in the study of these indicator sequences. Matrices leading to a constant R-sequence are called baseline matrices. They are characterized as matrices with constant w-year diachronous impact factors. The relation with classical impact factors is clarified. Using regression analysis matrices with a rhythm sequence that is on average equal to 1 (smaller than 1, larger than 1) are characterized.
  11. Lewison, G.: ¬The work of the Bibliometrics Research Group (City University) and associates (2005) 0.02
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    Date
    20. 1.2007 17:02:22
  12. Harries, G.; Wilkinson, D.; Price, L.; Fairclough, R.; Thelwall, M.: Hyperlinks as a data source for science mapping : making sense of it all (2005) 0.01
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  13. Schreiber, M.: ¬An empirical investigation of the g-index for 26 physicists in comparison with the h-index, the A-index, and the R-index (2008) 0.01
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    Abstract
    J.E. Hirsch (2005) introduced the h-index to quantify an individual's scientific research output by the largest number h of a scientist's papers that received at least h citations. To take into account the highly skewed frequency distribution of citations, L. Egghe (2006a) proposed the g-index as an improvement of the h-index. I have worked out 26 practical cases of physicists from the Institute of Physics at Chemnitz University of Technology, and compare the h and g values in this study. It is demonstrated that the g-index discriminates better between different citation patterns. This also can be achieved by evaluating B.H. Jin's (2006) A-index, which reflects the average number of citations in the h-core, and interpreting it in conjunction with the h-index. h and A can be combined into the R-index to measure the h-core's citation intensity. I also have determined the A and R values for the 26 datasets. For a better comparison, I utilize interpolated indices. The correlations between the various indices as well as with the total number of papers and the highest citation counts are discussed. The largest Pearson correlation coefficient is found between g and R. Although the correlation between g and h is relatively strong, the arrangement of the datasets is significantly different depending on whether they are put into order according to the values of either h or g.
    Object
    R-Index
  14. Rousseau, R.: On Egghe's construction of Lorenz curves (2007) 0.01
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  15. Grazia Colonia; Dimmler, E.; Dresel, R.; Messner, C.; Krobath, A.; Petz, S.; Sypien, M.; Boxen, P. van; Harders, M.; Heuer, D.; Jordans, I.; Juchem, K.; Linnertz, M.; Mittelhuber, I.; Schwammel, S.; Schlögl, C.; Stock, W.G.: Informationswissenschaftliche Zeitschriften in szientometrischer Analyse (2002) 0.01
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    Type
    r
  16. Raan, A.F.J. van: Statistical properties of bibliometric indicators : research group indicator distributions and correlations (2006) 0.01
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    Date
    22. 7.2006 16:20:22
  17. Larivière, V.; Gingras, Y.; Archambault, E.: ¬The decline in the concentration of citations, 1900-2007 (2009) 0.01
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    Date
    22. 3.2009 19:22:35
  18. Thelwall, M.; Ruschenburg, T.: Grundlagen und Forschungsfelder der Webometrie (2006) 0.01
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    Date
    4.12.2006 12:12:22
  19. Rostaing, H.; Barts, N.; Léveillé, V.: Bibliometrics: representation instrument of the multidisciplinary positioning of a scientific area : Implementation for an Advisory Scientific Committee (2007) 0.01
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    Date
    30.12.2007 11:22:39
  20. Weeber, M.; Klein, H.; Jong-van den Berg, L.T.W. de; Vos, R.: Using concepts in literature-based discovery : simulating Swanson's Raynaud-Fish Oil and Migraine-Manesium discoveries (2001) 0.01
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    Abstract
    Literature-based discovery has resulted in new knowledge. In the biomedical context, Don R. Swanson has generated several literature-based hypotheses that have been corroborated experimentally and clinically. In this paper, we propose a two-step model of the discovery process in which hypotheses are generated and subsequently tested. We have implemented this model in a Natural Language Processing system that uses biomedical Unified Medical Language System (UMLS) concepts as its unit of analysis. We use the semantic information that is provided with these concepts as a powerful filter to successfully simulate Swanson's discoveries of connecting Raynaud's disease with fish oil and migraine with a magnesium deficiency

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